{
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  "metadata": {
    "colab": {
      "name": "02_pytorch_classification_video.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNSFQyw5ZIDs6dTbkog11Nm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/video_notebooks/02_pytorch_classification_video.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 02. Neural Network classification with PyTorch\n",
        "\n",
        "Classification is a problem of predicting whether something is one thing or another (there can be multiple things as the options).\n",
        "\n",
        "* Book version of this notebook - https://www.learnpytorch.io/02_pytorch_classification/\n",
        "* All other resources - https://github.com/mrdbourke/pytorch-deep-learning\n",
        "* Stuck? Ask a question - https://github.com/mrdbourke/pytorch-deep-learning/discussions"
      ],
      "metadata": {
        "id": "BZ0Xn7qVenp8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Make classification data and get it ready"
      ],
      "metadata": {
        "id": "0exlIszJfBFB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn"
      ],
      "metadata": {
        "id": "ywkhYun3fW3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_circles\n",
        "\n",
        "# Make 1000 samples\n",
        "n_samples = 1000\n",
        "\n",
        "# Create circles\n",
        "X, y = make_circles(n_samples,\n",
        "                    noise=0.03,\n",
        "                    random_state=42)"
      ],
      "metadata": {
        "id": "o5ssZfgjfUC2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(X), len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96fM0MUYfrhg",
        "outputId": "02fcd93b-2c04-48d5-b73d-de432903120d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 1000)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"First 5 samples of X:\\n {X[:5]}\")\n",
        "print(f\"First 5 samples of y:\\n {y[:5]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ew1Hds-dftUS",
        "outputId": "8ee6a91e-1f01-4563-a524-20903b03820f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 5 samples of X:\n",
            " [[ 0.75424625  0.23148074]\n",
            " [-0.75615888  0.15325888]\n",
            " [-0.81539193  0.17328203]\n",
            " [-0.39373073  0.69288277]\n",
            " [ 0.44220765 -0.89672343]]\n",
            "First 5 samples of y:\n",
            " [1 1 1 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make DataFrame of circle data\n",
        "import pandas as pd\n",
        "circles = pd.DataFrame({\"X1\": X[:, 0], \n",
        "                        \"X2\": X[:, 1],\n",
        "                        \"label\": y})\n",
        "circles.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "yCU7lYZ9gFAf",
        "outputId": "fb60f6e0-905f-4ca7-a586-a697bf010503"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         X1        X2  label\n",
              "0  0.754246  0.231481      1\n",
              "1 -0.756159  0.153259      1\n",
              "2 -0.815392  0.173282      1\n",
              "3 -0.393731  0.692883      1\n",
              "4  0.442208 -0.896723      0\n",
              "5 -0.479646  0.676435      1\n",
              "6 -0.013648  0.803349      1\n",
              "7  0.771513  0.147760      1\n",
              "8 -0.169322 -0.793456      1\n",
              "9 -0.121486  1.021509      0"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
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              "      <th>0</th>\n",
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              "      <td>0.231481</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
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              "      <td>0.153259</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.815392</td>\n",
              "      <td>0.173282</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.393731</td>\n",
              "      <td>0.692883</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.442208</td>\n",
              "      <td>-0.896723</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.479646</td>\n",
              "      <td>0.676435</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-0.013648</td>\n",
              "      <td>0.803349</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.771513</td>\n",
              "      <td>0.147760</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-0.169322</td>\n",
              "      <td>-0.793456</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-0.121486</td>\n",
              "      <td>1.021509</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "circles.label.value_counts() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SBq9yzBj6rOH",
        "outputId": "939bbef3-fde7-4537-8f52-71016dd33286"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    500\n",
              "0    500\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize, visualize, visualize\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(x=X[:, 0],\n",
        "            y=X[:, 1],\n",
        "            c=y,\n",
        "            cmap=plt.cm.RdYlBu);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ACyUNaFlglL0",
        "outputId": "c5ec5c19-1eb2-4740-9661-08da35d6e361"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:** The data we're working with is often referred to as a toy dataset, a dataset that is small enough to experiment but still sizeable enough to practice the fundamentals."
      ],
      "metadata": {
        "id": "nTdbfhlehI9x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.1 Check input and output shapes"
      ],
      "metadata": {
        "id": "08jHXdIKgyPF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gOiNSewgyMo",
        "outputId": "359103bb-5709-4829-8c81-4c1294fe654f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1000, 2), (1000,))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_cR6eofgyKV",
        "outputId": "27254a8d-d8dc-4b59-afa3-86c6ea1bc5ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.75424625,  0.23148074],\n",
              "       [-0.75615888,  0.15325888],\n",
              "       [-0.81539193,  0.17328203],\n",
              "       ...,\n",
              "       [-0.13690036, -0.81001183],\n",
              "       [ 0.67036156, -0.76750154],\n",
              "       [ 0.28105665,  0.96382443]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View the first example of features and labels\n",
        "X_sample = X[0]\n",
        "y_sample = y[0]\n",
        "\n",
        "print(f\"Values for one sample of X: {X_sample} and the same for y: {y_sample}\")\n",
        "print(f\"Shapes for one sample of X: {X_sample.shape} and the same for y: {y_sample.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtCuPtJqgyID",
        "outputId": "95bcdc30-faba-4db4-98c0-dfe3728db0bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Values for one sample of X: [0.75424625 0.23148074] and the same for y: 1\n",
            "Shapes for one sample of X: (2,) and the same for y: ()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.2 Turn data into tensors and create train and test splits"
      ],
      "metadata": {
        "id": "XJHKlEnBgx4a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "uR42UiPSgx1E",
        "outputId": "59eabe77-0ff3-4080-9d15-c8a2d1838f1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.10.0+cu111'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(X), X.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VM9Hw2wljGEm",
        "outputId": "1cdfc733-78f3-45c1-c381-d99ac64df056"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(numpy.ndarray, dtype('float64'))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn data into tensors\n",
        "X = torch.from_numpy(X).type(torch.float)\n",
        "y = torch.from_numpy(y).type(torch.float)\n",
        "\n",
        "X[:5], y[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3v5Q3sIQgxuk",
        "outputId": "d280094f-fbc4-4aa6-a814-637be596e684"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[ 0.7542,  0.2315],\n",
              "         [-0.7562,  0.1533],\n",
              "         [-0.8154,  0.1733],\n",
              "         [-0.3937,  0.6929],\n",
              "         [ 0.4422, -0.8967]]), tensor([1., 1., 1., 1., 0.]))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(X), X.dtype, y.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpttifEogxpI",
        "outputId": "bde79db9-8f46-436e-e78a-a431625259d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Tensor, torch.float32, torch.float32)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into training and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, \n",
        "                                                    y,\n",
        "                                                    test_size=0.2, # 0.2 = 20% of data will be test & 80% will be train\n",
        "                                                    random_state=42) "
      ],
      "metadata": {
        "id": "cSDhwhz4i7mg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train), len(X_test), len(y_train), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4x1fQVaVi7jW",
        "outputId": "3e7f662c-bd00-454c-952e-1f0942d6655c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(800, 200, 800, 200)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilZT21YrjgH9",
        "outputId": "635f2894-5f15-4637-8b25-d822c176d585"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Building a model\n",
        "\n",
        "Let's build a model to classify our blue and red dots.\n",
        "\n",
        "To do so, we want to:\n",
        "1. Setup device agonistic code so our code will run on an accelerator (GPU) if there is one\n",
        "2. Construct a model (by subclassing `nn.Module`)\n",
        "3. Define a loss function and optimizer\n",
        "4. Create a training and test loop"
      ],
      "metadata": {
        "id": "L_NcXbrLjgEx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import PyTorch and nn\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# Make device agnostic code\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "gntGKE7KE8sD",
        "outputId": "360163ef-1438-4a4b-d3d7-37c00d064027"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oyl1BdAE8qI",
        "outputId": "764f18cc-7fc6-41fc-fd71-f74f4873d6a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.6579, -0.4651],\n",
              "        [ 0.6319, -0.7347],\n",
              "        [-1.0086, -0.1240],\n",
              "        ...,\n",
              "        [ 0.0157, -1.0300],\n",
              "        [ 1.0110,  0.1680],\n",
              "        [ 0.5578, -0.5709]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we've setup device agnostic code, let's create a model that:\n",
        "\n",
        "1. Subclasses `nn.Module` (almost all models in PyTorch subclass `nn.Module`)\n",
        "2. Create 2 `nn.Linear()` layers that are capable of handling the shapes of our data\n",
        "3. Defines a `forward()` method that outlines the forward pass (or forward computation) of the model\n",
        "4. Instatiate an instance of our model class and send it to the target `device`"
      ],
      "metadata": {
        "id": "XAuzqO7DjgBz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zEjsSZWHCWW",
        "outputId": "0a60a98f-ae43-48e4-bd8b-423ba35a58b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([800, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kNjO5I2LHXCd",
        "outputId": "19a3d9dd-aaa6-4a30-a310-4aef9d61da88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1., 0., 0., 0., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "# 1. Construct a model that subclasses nn.Module\n",
        "class CircleModelV0(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    # 2. Create 2 nn.Linear layers capable of handling the shapes of our data\n",
        "    self.layer_1 = nn.Linear(in_features=2, out_features=5) # takes in 2 features and upscales to 5 features \n",
        "    self.layer_2 = nn.Linear(in_features=5, out_features=1) # takes in 5 features from previous layer and outputs a single feature (same shape as y)\n",
        "\n",
        "  # 3. Define a forward() method that outlines the forward pass\n",
        "  def forward(self, x):\n",
        "    return self.layer_2(self.layer_1(x)) # x -> layer_1 ->  layer_2 -> output\n",
        "\n",
        "# 4. Instantiate an instance of our model class and send it to the target device\n",
        "model_0 = CircleModelV0().to(device)\n",
        "model_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIN-TOvYGZBf",
        "outputId": "91244b91-0a56-41d8-b9b4-d58b267dd750"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CircleModelV0(\n",
              "  (layer_1): Linear(in_features=2, out_features=5, bias=True)\n",
              "  (layer_2): Linear(in_features=5, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "THHrI9iPGY_B",
        "outputId": "75dbd78f-0c3b-4683-c4df-44d81eaf54ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next(model_0.parameters()).device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VThEgNG_GY8k",
        "outputId": "f4c0b40c-96bc-4840-c451-a95614eb7ecb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's replicate the model above using nn.Sequential()\n",
        "model_0 = nn.Sequential(\n",
        "    nn.Linear(in_features=2, out_features=5),\n",
        "    nn.Linear(in_features=5, out_features=1)\n",
        ").to(device)\n",
        "\n",
        "model_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UW0ZATKnGY0f",
        "outputId": "f6991df0-4688-455c-8e5a-3eef27945aaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Linear(in_features=2, out_features=5, bias=True)\n",
              "  (1): Linear(in_features=5, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nOKuT23HMUGd",
        "outputId": "124d0768-f597-4e5c-d32a-53425b57e317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('0.weight', tensor([[-0.1962,  0.3652],\n",
              "                      [ 0.2764,  0.2147],\n",
              "                      [ 0.5723, -0.5955],\n",
              "                      [-0.2329,  0.0170],\n",
              "                      [ 0.6259,  0.6916]], device='cuda:0')),\n",
              "             ('0.bias',\n",
              "              tensor([ 0.4193,  0.6624,  0.2594, -0.1640, -0.0477], device='cuda:0')),\n",
              "             ('1.weight',\n",
              "              tensor([[ 0.4129,  0.2358,  0.4115,  0.4045, -0.0853]], device='cuda:0')),\n",
              "             ('1.bias', tensor([-0.4294], device='cuda:0'))])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "with torch.inference_mode():\n",
        "  untrained_preds = model_0(X_test.to(device))\n",
        "print(f\"Length of predictions: {len(untrained_preds)}, Shape: {untrained_preds.shape}\")\n",
        "print(f\"Length of test samples: {len(X_test)}, Shape: {X_test.shape}\")\n",
        "print(f\"\\nFirst 10 predictions:\\n{torch.round(untrained_preds[:10])}\")\n",
        "print(f\"\\nFirst 10 labels:\\n{y_test[:10]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9HpguZ1k5LR",
        "outputId": "bd51ea62-a418-4fb6-8229-5ee2cddcb0d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of predictions: 200, Shape: torch.Size([200, 1])\n",
            "Length of test samples: 200, Shape: torch.Size([200, 2])\n",
            "\n",
            "First 10 predictions:\n",
            "tensor([[-0.],\n",
            "        [-0.],\n",
            "        [-0.],\n",
            "        [-0.],\n",
            "        [0.],\n",
            "        [0.],\n",
            "        [-0.],\n",
            "        [-0.],\n",
            "        [-0.],\n",
            "        [-0.]], device='cuda:0')\n",
            "\n",
            "First 10 labels:\n",
            "tensor([1., 0., 1., 0., 1., 1., 0., 0., 1., 0.])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[:10], y_test[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DN7RbDmRk5Iy",
        "outputId": "8ab196d3-5d95-4cf3-f662-19050b609edb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[-0.3752,  0.6827],\n",
              "         [ 0.0154,  0.9600],\n",
              "         [-0.7028, -0.3147],\n",
              "         [-0.2853,  0.9664],\n",
              "         [ 0.4024, -0.7438],\n",
              "         [ 0.6323, -0.5711],\n",
              "         [ 0.8561,  0.5499],\n",
              "         [ 1.0034,  0.1903],\n",
              "         [-0.7489, -0.2951],\n",
              "         [ 0.0538,  0.9739]]),\n",
              " tensor([1., 0., 1., 0., 1., 1., 0., 0., 1., 0.]))"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.1 Setup loss function and optimizer\n",
        "\n",
        "Which loss function or optimizer should you use?\n",
        "\n",
        "Again... this is problem specific.\n",
        "\n",
        "For example for regression you might want MAE or MSE (mean absolute error or mean squared error).\n",
        "\n",
        "For classification you might want binary cross entropy or categorical cross entropy (cross entropy).\n",
        "\n",
        "As a reminder, the loss function measures how *wrong* your models predictions are.\n",
        "\n",
        "And for optimizers, two of the most common and useful are SGD and Adam, however PyTorch has many built-in options.\n",
        "\n",
        "* For some common choices of loss functions and optimizers - https://www.learnpytorch.io/02_pytorch_classification/#21-setup-loss-function-and-optimizer\n",
        "* For the loss function we're going to use `torch.nn.BECWithLogitsLoss()`, for more on what binary cross entropy (BCE) is, check out this article - https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a \n",
        "* For a defintion on what a logit is in deep learning - https://stackoverflow.com/a/52111173/7900723 \n",
        "* For different optimizers see `torch.optim`"
      ],
      "metadata": {
        "id": "yLWkObPUk5GZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup the loss function\n",
        "# loss_fn = nn.BCELoss() # BCELoss = requires inputs to have gone through the sigmoid activation function prior to input to BCELoss\n",
        "loss_fn = nn.BCEWithLogitsLoss() # BCEWithLogitsLoss = sigmoid activation function built-in\n",
        "\n",
        "optimizer = torch.optim.SGD(params=model_0.parameters(),\n",
        "                            lr=0.1)"
      ],
      "metadata": {
        "id": "XzlvBHAxk5D3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate accuracy - out of 100 examples, what percentage does our model get right? \n",
        "def accuracy_fn(y_true, y_pred):\n",
        "  correct = torch.eq(y_true, y_pred).sum().item() \n",
        "  acc = (correct/len(y_pred)) * 100\n",
        "  return acc"
      ],
      "metadata": {
        "id": "3yd16y0lT4Uq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Train model \n",
        "\n",
        "To train our model, we're going to need to build a training loop with the following steps:\n",
        "\n",
        "1. Forward pass\n",
        "2. Calculate the loss\n",
        "3. Optimizer zero grad\n",
        "4. Loss backward (backpropagation)\n",
        "5. Optimizer step (gradient descent) "
      ],
      "metadata": {
        "id": "cF3KAKiuUath"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Going from raw logits -> prediction probabilities -> prediction labels\n",
        "\n",
        "Our model outputs are going to be raw **logits**.\n",
        "\n",
        "We can convert these **logits** into **prediction probabilities** by passing them to some kind of activation function (e.g. sigmoid for binary classification and softmax for multiclass classification).\n",
        "\n",
        "Then we can convert our model's prediction probabilities to **prediction labels** by either rounding them or taking the `argmax()`."
      ],
      "metadata": {
        "id": "D2APTiyUV4LL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# View the first 5 outputs of the forward pass on the test data\n",
        "model_0.eval() \n",
        "with torch.inference_mode():\n",
        "  y_logits = model_0(X_test.to(device))[:5]\n",
        "y_logits "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Yg_6s5wWbnS",
        "outputId": "cad92487-6c45-4393-cc3f-f7f1643bf576"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.1481],\n",
              "        [-0.1465],\n",
              "        [-0.0762],\n",
              "        [-0.1688],\n",
              "        [ 0.0445]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LiLz-TDzWg53",
        "outputId": "5168fa29-1bdb-4a5a-9a07-3a0fdfde7d93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1., 0., 1., 0., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the sigmoid activation function on our model logits to turn them into prediction probabilities\n",
        "y_pred_probs = torch.sigmoid(y_logits)\n",
        "y_pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrDZmTcZXiYu",
        "outputId": "2eddc652-de79-4cc0-e54b-5531d7daaf68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.4630],\n",
              "        [0.4634],\n",
              "        [0.4810],\n",
              "        [0.4579],\n",
              "        [0.5111]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For our prediction probability values, we need to perform a range-style rounding on them:\n",
        "* `y_pred_probs` >= 0.5, `y=1` (class 1)\n",
        "* `y_pred_probs` < 0.5, `y=0` (class 0)"
      ],
      "metadata": {
        "id": "f-vYzxKwX_G1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the predicted labels \n",
        "y_preds = torch.round(y_pred_probs)\n",
        "\n",
        "# In full (logits -> pred probs -> pred labels)\n",
        "y_pred_labels = torch.round(torch.sigmoid(model_0(X_test.to(device))[:5]))\n",
        "\n",
        "# Check for equality\n",
        "print(torch.eq(y_preds.squeeze(), y_pred_labels.squeeze()))\n",
        "\n",
        "# Get rid of extra dimension\n",
        "y_preds.squeeze()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WQb8sp_Xwey",
        "outputId": "cc780c25-5119-484a-9075-27b60aaec6f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([True, True, True, True, True], device='cuda:0')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0., 0., 0., 0., 1.], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXkhLOT5X1wq",
        "outputId": "d46fb129-17df-471b-8ab9-eff681307a43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1., 0., 1., 0., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Building a training and testing loop"
      ],
      "metadata": {
        "id": "u7pb-y3OZG5b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42) \n",
        "\n",
        "# Set the number of epochs\n",
        "epochs = 100\n",
        "\n",
        "# Put data to target device \n",
        "X_train, y_train = X_train.to(device), y_train.to(device)\n",
        "X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "\n",
        "# Build training and evaluation loop\n",
        "for epoch in range(epochs):\n",
        "  ### Training\n",
        "  model_0.train()\n",
        "\n",
        "  # 1. Forward pass\n",
        "  y_logits = model_0(X_train).squeeze()\n",
        "  y_pred = torch.round(torch.sigmoid(y_logits)) # turn logits -> pred probs -> pred labels\n",
        "\n",
        "  # 2. Calculate loss/accuracy\n",
        "  # loss = loss_fn(torch.sigmoid(y_logits), # nn.BCELoss expects prediction probabilities as input\n",
        "  #                y_train)\n",
        "  loss = loss_fn(y_logits, # nn.BCEWithLogitsLoss expects raw logits as input\n",
        "                 y_train)\n",
        "  acc = accuracy_fn(y_true=y_train, \n",
        "                    y_pred=y_pred)\n",
        "  \n",
        "  # 3. Optimizer zero grad\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  # 4. Loss backward (backpropagation)\n",
        "  loss.backward()\n",
        "\n",
        "  # 5. Optimizer step (gradient descent)\n",
        "  optimizer.step() \n",
        "\n",
        "  ### Testing\n",
        "  model_0.eval()\n",
        "  with torch.inference_mode():\n",
        "    # 1. Forward pass \n",
        "    test_logits = model_0(X_test).squeeze()\n",
        "    test_pred = torch.round(torch.sigmoid(test_logits))\n",
        "\n",
        "    # 2. Calculate test loss/acc\n",
        "    test_loss = loss_fn(test_logits,\n",
        "                        y_test)\n",
        "    test_acc = accuracy_fn(y_true=y_test,\n",
        "                           y_pred=test_pred)\n",
        "  \n",
        "  # Print out what's happenin'\n",
        "  if epoch % 10 == 0:\n",
        "    print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Acc: {acc:.2f}% | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DxUjouKwZMRl",
        "outputId": "8423d8f6-60aa-4dc6-8d2a-5b4dc3baf889"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Loss: 0.69461, Acc: 47.88% | Test loss: 0.69301, Test acc: 48.50%\n",
            "Epoch: 10 | Loss: 0.69402, Acc: 48.75% | Test loss: 0.69296, Test acc: 48.00%\n",
            "Epoch: 20 | Loss: 0.69369, Acc: 49.50% | Test loss: 0.69308, Test acc: 46.50%\n",
            "Epoch: 30 | Loss: 0.69348, Acc: 50.62% | Test loss: 0.69325, Test acc: 45.50%\n",
            "Epoch: 40 | Loss: 0.69334, Acc: 50.12% | Test loss: 0.69341, Test acc: 48.50%\n",
            "Epoch: 50 | Loss: 0.69324, Acc: 49.25% | Test loss: 0.69357, Test acc: 51.00%\n",
            "Epoch: 60 | Loss: 0.69317, Acc: 49.50% | Test loss: 0.69372, Test acc: 50.50%\n",
            "Epoch: 70 | Loss: 0.69312, Acc: 50.38% | Test loss: 0.69385, Test acc: 49.00%\n",
            "Epoch: 80 | Loss: 0.69308, Acc: 50.12% | Test loss: 0.69396, Test acc: 49.50%\n",
            "Epoch: 90 | Loss: 0.69305, Acc: 50.50% | Test loss: 0.69406, Test acc: 48.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Make predictions and evaluate the model\n",
        "\n",
        "From the metrics it looks like our model isn't learning anything... \n",
        "\n",
        "So to inspect it let's make some predictions and make them visual! \n",
        "\n",
        "In other words, \"Visualize, visualize, visualize!\"\n",
        "\n",
        "To do so, we're going to import a function called `plot_decision_boundary()` - https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py "
      ],
      "metadata": {
        "id": "ud5xW_kM3xtH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from pathlib import Path\n",
        "\n",
        "# Download helper functions from Learn PyTorch repo (if it's not already downloaded)\n",
        "if Path(\"helper_functions.py\").is_file():\n",
        "  print(\"helper_functions.py already exists, skipping download\")\n",
        "else:\n",
        "  print(\"Downloading helper_functions.py\")\n",
        "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
        "  with open(\"helper_functions.py\", \"wb\") as f:\n",
        "    f.write(request.content)\n",
        "\n",
        "from helper_functions import plot_predictions, plot_decision_boundary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NhFdb1RX7Flu",
        "outputId": "d0272124-e6dc-490e-fd8b-32326ec684ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading helper_functions.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot decision boundary of the model\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_0, X_train, y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_0, X_test, y_test) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "8V7KyDQ78YxA",
        "outputId": "028beb0f-22b6-4f89-92de-113763a49a8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Improving a model (from a model perspective)\n",
        "\n",
        "* Add more layers - give the model more chances to learn about patterns in the data\n",
        "* Add more hidden units - go from 5 hidden units to 10 hidden units \n",
        "* Fit for longer\n",
        "* Changing the activation functions\n",
        "* Change the learning rate\n",
        "* Change the loss function \n",
        "\n",
        "These options are all from a model's perspective because they deal directly with the model, rather than the data.\n",
        "\n",
        "And because these options are all values we (as machine learning engineers and data scientists) can change, they are referred as **hyperparameters**.\n",
        "\n",
        "Let's try and improve our model by:\n",
        "* Adding more hidden units: 5 -> 10\n",
        "* Increase the number of layers: 2 -> 3\n",
        "* Increase the number of epochs: 100 -> 1000"
      ],
      "metadata": {
        "id": "RYn2XJ6I9BU3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[:5], y_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HyQySuuBcH0",
        "outputId": "f94a2cf8-c7dd-4bbe-896b-4a932662ece0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[ 0.6579, -0.4651],\n",
              "         [ 0.6319, -0.7347],\n",
              "         [-1.0086, -0.1240],\n",
              "         [-0.9666, -0.2256],\n",
              "         [-0.1666,  0.7994]], device='cuda:0'),\n",
              " tensor([1., 0., 0., 0., 1.], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a model\n",
        "class CircleModelV1(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.layer_1 = nn.Linear(in_features=2, out_features=10) \n",
        "    self.layer_2 = nn.Linear(in_features=10, out_features=10)\n",
        "    self.layer_3 = nn.Linear(in_features=10, out_features=1)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    # z = self.layer_1(x)\n",
        "    # z = self.layer_2(z)\n",
        "    # z = self.layer_3(z) \n",
        "    return self.layer_3(self.layer_2(self.layer_1(x))) # this way of writing operations leverages speed ups where possible behind the scenes\n",
        "\n",
        "model_1 = CircleModelV1().to(device)\n",
        "model_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7l3EDkwm-EPe",
        "outputId": "fa886d3f-8eed-4cc8-d6fd-16754589efc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CircleModelV1(\n",
              "  (layer_1): Linear(in_features=2, out_features=10, bias=True)\n",
              "  (layer_2): Linear(in_features=10, out_features=10, bias=True)\n",
              "  (layer_3): Linear(in_features=10, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a loss function\n",
        "loss_fn = nn.BCEWithLogitsLoss()\n",
        "\n",
        "# Create an optimizer \n",
        "optimizer = torch.optim.SGD(params=model_1.parameters(), \n",
        "                            lr=0.1)"
      ],
      "metadata": {
        "id": "T_6K4KoD-MOS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Write a training and evaluation loop for model_1\n",
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42) \n",
        "\n",
        "# Train for longer\n",
        "epochs = 1000\n",
        "\n",
        "# Put data on the target device\n",
        "X_train, y_train = X_train.to(device), y_train.to(device)\n",
        "X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  ### Training\n",
        "  model_1.train()\n",
        "  # 1. Forward pass\n",
        "  y_logits = model_1(X_train).squeeze()\n",
        "  y_pred = torch.round(torch.sigmoid(y_logits)) # logits -> pred probabilities -> prediction labels\n",
        "\n",
        "  # 2. Calculate the loss/acc\n",
        "  loss = loss_fn(y_logits, y_train)\n",
        "  acc = accuracy_fn(y_true=y_train,\n",
        "                    y_pred=y_pred)\n",
        "  \n",
        "  # 3. Optimizer zero grad\n",
        "  optimizer.zero_grad() \n",
        "\n",
        "  # 4. Loss backward (backpropagation) \n",
        "  loss.backward()\n",
        "\n",
        "  # 5. Optimizer step (gradient descent)\n",
        "  optimizer.step()\n",
        "\n",
        "  ### Testing\n",
        "  model_1.eval()\n",
        "  with torch.inference_mode():\n",
        "    # 1. Forward pass\n",
        "    test_logits = model_1(X_test).squeeze()\n",
        "    test_pred = torch.round(torch.sigmoid(test_logits)) \n",
        "    # 2. Calculate loss\n",
        "    test_loss = loss_fn(test_logits,\n",
        "                        y_test)\n",
        "    test_acc = accuracy_fn(y_true=y_test,\n",
        "                           y_pred=test_pred)\n",
        "\n",
        "  # Print out what's happenin'\n",
        "  if epoch % 100 == 0:\n",
        "    print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Acc: {acc:.2f}% | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCVLN4FiCcnn",
        "outputId": "cbefd531-e186-4b1e-fa78-49e3d6918fe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Loss: 0.69396, Acc: 50.88% | Test loss: 0.69261, Test acc: 51.00%\n",
            "Epoch: 100 | Loss: 0.69305, Acc: 50.38% | Test loss: 0.69379, Test acc: 48.00%\n",
            "Epoch: 200 | Loss: 0.69299, Acc: 51.12% | Test loss: 0.69437, Test acc: 46.00%\n",
            "Epoch: 300 | Loss: 0.69298, Acc: 51.62% | Test loss: 0.69458, Test acc: 45.00%\n",
            "Epoch: 400 | Loss: 0.69298, Acc: 51.12% | Test loss: 0.69465, Test acc: 46.00%\n",
            "Epoch: 500 | Loss: 0.69298, Acc: 51.00% | Test loss: 0.69467, Test acc: 46.00%\n",
            "Epoch: 600 | Loss: 0.69298, Acc: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
            "Epoch: 700 | Loss: 0.69298, Acc: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
            "Epoch: 800 | Loss: 0.69298, Acc: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
            "Epoch: 900 | Loss: 0.69298, Acc: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot decision boundary of the model\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_1, X_train, y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_1, X_test, y_test) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "NJp4kN83EiVt",
        "outputId": "af4a3915-8938-4f7e-99ea-b1d8ce12fcfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.1 Preparing data to see if our model can fit a straight line\n",
        "\n",
        "One way to troubleshoot to a larger problem is to test out a smaller problem."
      ],
      "metadata": {
        "id": "Le-cKAwaEiSi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create some data (same as notebook 01)\n",
        "weight = 0.7\n",
        "bias = 0.3\n",
        "start = 0\n",
        "end = 1\n",
        "step = 0.01\n",
        "\n",
        "# Create data\n",
        "X_regression = torch.arange(start, end, step).unsqueeze(dim=1)\n",
        "y_regression = weight * X_regression + bias # Linear regression formula (without epsilon)\n",
        "\n",
        "# Check the data\n",
        "print(len(X_regression))\n",
        "X_regression[:5], y_regression[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGabtpZ-EiPu",
        "outputId": "69b1e3e1-84d8-42e3-9da5-1e0dd4d19a88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[0.0000],\n",
              "         [0.0100],\n",
              "         [0.0200],\n",
              "         [0.0300],\n",
              "         [0.0400]]), tensor([[0.3000],\n",
              "         [0.3070],\n",
              "         [0.3140],\n",
              "         [0.3210],\n",
              "         [0.3280]]))"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create train and test splits\n",
        "train_split = int(0.8 * len(X_regression))\n",
        "X_train_regression, y_train_regression = X_regression[:train_split], y_regression[:train_split]\n",
        "X_test_regression, y_test_regression = X_regression[train_split:], y_regression[train_split:]\n",
        "\n",
        "# Check the lengths of each\n",
        "len(X_train_regression), len(X_test_regression), len(y_train_regression), len(y_test_regression)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSrnNiFWEiMa",
        "outputId": "a329ff6d-70a1-461c-dad0-753d3ece3cdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80, 20, 80, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(train_data=X_train_regression, \n",
        "                 train_labels=y_train_regression,\n",
        "                 test_data=X_test_regression,\n",
        "                 test_labels=y_test_regression);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "wG1Dhz9SEiJD",
        "outputId": "36136bca-65af-461c-b95a-117315591eba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.2 Adjusting `model_1` to fit a straight line"
      ],
      "metadata": {
        "id": "usxOtv8tGlaZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Same architecture as model_1 (but using nn.Sequential())\n",
        "model_2 = nn.Sequential(\n",
        "    nn.Linear(in_features=1, out_features=10),\n",
        "    nn.Linear(in_features=10, out_features=10),\n",
        "    nn.Linear(in_features=10, out_features=1)\n",
        ").to(device)\n",
        "\n",
        "model_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRUPRukiGlYO",
        "outputId": "b7d432dc-6b1b-4973-f0f0-0508cd4f59d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Linear(in_features=1, out_features=10, bias=True)\n",
              "  (1): Linear(in_features=10, out_features=10, bias=True)\n",
              "  (2): Linear(in_features=10, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss and optimizer\n",
        "loss_fn = nn.L1Loss() # MAE loss with regression data\n",
        "optimizer = torch.optim.SGD(params=model_2.parameters(), \n",
        "                            lr=0.01)"
      ],
      "metadata": {
        "id": "twGWjcG4GlWJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)\n",
        "\n",
        "# Set the number of epochs\n",
        "epochs = 1000\n",
        "\n",
        "# Put the data on the target device\n",
        "X_train_regression, y_train_regression = X_train_regression.to(device), y_train_regression.to(device)\n",
        "X_test_regression, y_test_regression = X_test_regression.to(device), y_test_regression.to(device)\n",
        "\n",
        "# Training\n",
        "for epoch in range(epochs):\n",
        "  y_pred = model_2(X_train_regression)\n",
        "  loss = loss_fn(y_pred, y_train_regression)\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "\n",
        "  # Testing\n",
        "  model_2.eval()\n",
        "  with torch.inference_mode():\n",
        "    test_pred = model_2(X_test_regression)\n",
        "    test_loss = loss_fn(test_pred, y_test_regression)\n",
        "\n",
        "  # Print out what's happenin'\n",
        "  if epoch % 100 == 0:\n",
        "    print(f\"Epoch: {epoch} | Loss: {loss:.5f} | Test loss: {test_loss:.5f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cTT35QgkPP-",
        "outputId": "e386ee2a-aaa3-4a2f-f42d-abfd7d74bdf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Loss: 0.75986 | Test loss: 0.91103\n",
            "Epoch: 100 | Loss: 0.02858 | Test loss: 0.00081\n",
            "Epoch: 200 | Loss: 0.02533 | Test loss: 0.00209\n",
            "Epoch: 300 | Loss: 0.02137 | Test loss: 0.00305\n",
            "Epoch: 400 | Loss: 0.01964 | Test loss: 0.00341\n",
            "Epoch: 500 | Loss: 0.01940 | Test loss: 0.00387\n",
            "Epoch: 600 | Loss: 0.01903 | Test loss: 0.00379\n",
            "Epoch: 700 | Loss: 0.01878 | Test loss: 0.00381\n",
            "Epoch: 800 | Loss: 0.01840 | Test loss: 0.00329\n",
            "Epoch: 900 | Loss: 0.01798 | Test loss: 0.00360\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn on evaluation mode\n",
        "model_2.eval()\n",
        "\n",
        "# Make predictions (inference)\n",
        "with torch.inference_mode():\n",
        "  y_preds = model_2(X_test_regression)\n",
        "\n",
        "# Plot data and predictions\n",
        "plot_predictions(train_data=X_train_regression.cpu(), \n",
        "                 train_labels=y_train_regression.cpu(),\n",
        "                 test_data=X_test_regression.cpu(),\n",
        "                 test_labels=y_test_regression.cpu(),\n",
        "                 predictions=y_preds.cpu()); "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "TiaYv8p-lPV2",
        "outputId": "34eef5f9-6399-4868-931c-7447d5aa33b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. The missing piece: non-linearity \n",
        "\n",
        "\"What patterns could you draw if you were given an infinite amount of a straight and non-straight lines?\"\n",
        "\n",
        "Or in machine learning terms, an infinite (but really it is finite) of linear and non-linear functions?"
      ],
      "metadata": {
        "id": "HMvNNwSJmm98"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.1 Recreating non-linear data (red and blue circles)"
      ],
      "metadata": {
        "id": "j_P9_-1apMWq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make and plot data\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_circles\n",
        "\n",
        "n_samples = 1000\n",
        "\n",
        "X, y = make_circles(n_samples,\n",
        "                    noise=0.03,\n",
        "                    random_state=42)\n",
        "\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "TfUWJPWBpo4-",
        "outputId": "9ccbe059-1eb8-4a01-cc79-e4b49908e226"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert data to tensors and then to train and test splits \n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Turn data into tensors\n",
        "X = torch.from_numpy(X).type(torch.float) \n",
        "y = torch.from_numpy(y).type(torch.float)\n",
        "\n",
        "# Split into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
        "                                                    y, \n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=42) \n",
        "\n",
        "X_train[:5], y_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pocAXPCp7o2",
        "outputId": "29676b6f-4ce3-4833-998e-67e11daf9fc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[ 0.6579, -0.4651],\n",
              "         [ 0.6319, -0.7347],\n",
              "         [-1.0086, -0.1240],\n",
              "         [-0.9666, -0.2256],\n",
              "         [-0.1666,  0.7994]]), tensor([1., 0., 0., 0., 1.]))"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.2 Building a model with non-linearity\n",
        "\n",
        "* Linear = straight lines\n",
        "* Non-linear = non-straight lines\n",
        "\n",
        "Artificial neural networks are a large combination of linear (straight) and non-straight (non-linear) functions which are potentially able to find patterns in data."
      ],
      "metadata": {
        "id": "TbVSE-VeqjsG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a model with non-linear activation functions\n",
        "from torch import nn\n",
        "class CircleModelV2(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.layer_1 = nn.Linear(in_features=2, out_features=10)\n",
        "    self.layer_2 = nn.Linear(in_features=10, out_features=10)\n",
        "    self.layer_3 = nn.Linear(in_features=10, out_features=1)\n",
        "    self.relu = nn.ReLU() # relu is a non-linear activation function\n",
        "    \n",
        "  def forward(self, x):\n",
        "    # Where should we put our non-linear activation functions?\n",
        "    return self.layer_3(self.relu(self.layer_2(self.relu(self.layer_1(x)))))\n",
        "\n",
        "model_3 = CircleModelV2().to(device)\n",
        "model_3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENa01YTsF0Pt",
        "outputId": "c159eb01-baad-4393-8a1b-b3b0ebe73caf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CircleModelV2(\n",
              "  (layer_1): Linear(in_features=2, out_features=10, bias=True)\n",
              "  (layer_2): Linear(in_features=10, out_features=10, bias=True)\n",
              "  (layer_3): Linear(in_features=10, out_features=1, bias=True)\n",
              "  (relu): ReLU()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup loss and optimizer\n",
        "loss_fn = nn.BCEWithLogitsLoss()\n",
        "optimizer = torch.optim.SGD(model_3.parameters(), \n",
        "                            lr=0.1)"
      ],
      "metadata": {
        "id": "vUSJUD9AHyqZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.3 Training a model with non-linearity"
      ],
      "metadata": {
        "id": "32forBUPJcTd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_test), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-C0CpzWULlCo",
        "outputId": "71b19d1b-ebfc-42cb-fb18-e350e4c64c1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 200)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random seeds \n",
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)\n",
        "\n",
        "# Put all data on target device\n",
        "X_train, y_train = X_train.to(device), y_train.to(device)\n",
        "X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "\n",
        "# Loop through data\n",
        "epochs = 1000\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  ### Training\n",
        "  model_3.train()\n",
        "\n",
        "  # 1. Forward pass\n",
        "  y_logits = model_3(X_train).squeeze()\n",
        "  y_pred = torch.round(torch.sigmoid(y_logits)) # logits -> prediction probabilities -> prediction labels\n",
        "\n",
        "  # 2. Calculate the loss\n",
        "  loss = loss_fn(y_logits, y_train) # BCEWithLogitsLoss (takes in logits as first input)\n",
        "  acc = accuracy_fn(y_true=y_train,\n",
        "                    y_pred=y_pred)\n",
        "  \n",
        "  # 3. Optimizer zero grad\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  # 4. Loss backward\n",
        "  loss.backward()\n",
        "\n",
        "  # 5. Step the optimizer\n",
        "  optimizer.step()\n",
        "\n",
        "  ### Testing\n",
        "  model_3.eval()\n",
        "  with torch.inference_mode():\n",
        "    test_logits = model_3(X_test).squeeze()\n",
        "    test_pred = torch.round(torch.sigmoid(test_logits))\n",
        "    \n",
        "    test_loss = loss_fn(test_logits, y_test)\n",
        "    test_acc = accuracy_fn(y_true=y_test, \n",
        "                           y_pred=test_pred)\n",
        "  \n",
        "  # Print out what's this happenin'\n",
        "  if epoch % 100 == 0:\n",
        "    print(f\"Epoch: {epoch} | Loss: {loss:.4f}, Acc: {acc:.2f}% | Test Loss: {test_loss:.4f}, Test Acc: {test_acc:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kriIowDxJn0J",
        "outputId": "af712b14-5f68-48e6-ebee-39f17b9b4991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Loss: 0.6928, Acc: 50.00% | Test Loss: 0.6931, Test Acc: 50.00%\n",
            "Epoch: 100 | Loss: 0.6911, Acc: 53.12% | Test Loss: 0.6910, Test Acc: 53.00%\n",
            "Epoch: 200 | Loss: 0.6897, Acc: 53.50% | Test Loss: 0.6894, Test Acc: 55.50%\n",
            "Epoch: 300 | Loss: 0.6879, Acc: 53.00% | Test Loss: 0.6872, Test Acc: 56.00%\n",
            "Epoch: 400 | Loss: 0.6851, Acc: 52.75% | Test Loss: 0.6840, Test Acc: 56.50%\n",
            "Epoch: 500 | Loss: 0.6809, Acc: 52.75% | Test Loss: 0.6793, Test Acc: 56.50%\n",
            "Epoch: 600 | Loss: 0.6750, Acc: 54.62% | Test Loss: 0.6727, Test Acc: 56.50%\n",
            "Epoch: 700 | Loss: 0.6664, Acc: 58.38% | Test Loss: 0.6630, Test Acc: 59.50%\n",
            "Epoch: 800 | Loss: 0.6512, Acc: 64.25% | Test Loss: 0.6472, Test Acc: 68.00%\n",
            "Epoch: 900 | Loss: 0.6228, Acc: 74.00% | Test Loss: 0.6208, Test Acc: 79.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.4 Evaluating a model trained with non-linear activation functions"
      ],
      "metadata": {
        "id": "MYNiJIEtLCZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Makes predictions\n",
        "model_3.eval()\n",
        "with torch.inference_mode():\n",
        "  y_preds = torch.round(torch.sigmoid(model_3(X_test))).squeeze()\n",
        "y_preds[:10], y_test[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yX2i_RQtLCXC",
        "outputId": "09e2c9ab-1e39-404a-a41e-4025cf0eb879"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([1., 0., 1., 0., 0., 1., 0., 0., 1., 0.], device='cuda:0'),\n",
              " tensor([1., 0., 1., 0., 1., 1., 0., 0., 1., 0.], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot decision boundaries\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_1, X_train, y_train) # model_1 = no non-linearity\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_3, X_test, y_test) # model_3 = has non-linearity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "TNm377UdLCUP",
        "outputId": "a9cc4783-659c-47a0-ce87-89f169450dd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Challenge:** Can you improve model_3 to do better than 80% accuracy on the test data?"
      ],
      "metadata": {
        "id": "cVJLKGWzLCRb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Replicating non-linear activation functions\n",
        "\n",
        "Neural networks, rather than us telling the model what to learn, we give it the tools to discover patterns in data and it tries to figure out the patterns on its own.\n",
        "\n",
        "And these tools are linear & non-linear functions."
      ],
      "metadata": {
        "id": "TGHd76BYNmec"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a tensor\n",
        "A = torch.arange(-10, 10, 1, dtype=torch.float32)\n",
        "A.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XjXBtkWLNmaF",
        "outputId": "df98ea75-13c0-4d57-f86a-a99644f0b937"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.float32"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wA4ZpNyJu9b",
        "outputId": "736a48b9-efe7-4045-d623-65aef95d134e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,\n",
              "          2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the tensor\n",
        "plt.plot(A); "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "X3r8BSjGO8g_",
        "outputId": "a7a2409c-7f44-4e69-c915-9a54b7426af9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(torch.relu(A));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Gp_tFktUO8eI",
        "outputId": "4f15d40f-45be-4b8d-9ce0-98145f9af727"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKmMlFW1PvK9",
        "outputId": "897a5e61-5592-47a4-9fc0-3140f18f5c6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,\n",
              "          2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def relu(x: torch.Tensor) -> torch.Tensor:\n",
        "  return torch.maximum(torch.tensor(0), x) # inputs must be tensors\n",
        "\n",
        "relu(A)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_hdk3VY_O8a9",
        "outputId": "9d216d06-424a-4051-9874-cceebf46a0b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3., 4., 5., 6., 7.,\n",
              "        8., 9.])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot ReLU activation function\n",
        "plt.plot(relu(A)); "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "j29MXDwKPh1U",
        "outputId": "05fbef25-73fd-4761-b110-217d4183b6f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now let's do the same for Sigmoid = https://pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html#torch.nn.Sigmoid \n",
        "def sigmoid(x):\n",
        "  return 1 / (1 + torch.exp(-x))"
      ],
      "metadata": {
        "id": "FXR9tfc3Phwc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(torch.sigmoid(A)); "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "j5F30-gHP8Lw",
        "outputId": "51e03558-c303-45da-8266-672e4b6e8555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(sigmoid(A));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "68OTxhZmPhsN",
        "outputId": "3acbe937-8027-43b2-ad76-8f37028c88b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Putting it all together with a multi-class classification problem\n",
        "\n",
        "* Binary classification = one thing or another (cat vs. dog, spam vs. not spam, fraud or not fraud)\n",
        "* Multi-class classification = more than one thing or another (cat vs. dog vs. chicken)"
      ],
      "metadata": {
        "id": "OpHIZK9CPha_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.1 Creating a toy multi-class dataset"
      ],
      "metadata": {
        "id": "R_pdUtYsR4Um"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import dependencies \n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_blobs # https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs \n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Set the hyperparameters for data creation\n",
        "NUM_CLASSES = 4\n",
        "NUM_FEATURES = 2\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "# 1. Create multi-class data\n",
        "X_blob, y_blob = make_blobs(n_samples=1000,\n",
        "                            n_features=NUM_FEATURES,\n",
        "                            centers=NUM_CLASSES,\n",
        "                            cluster_std=1.5, # give the clusters a little shake up\n",
        "                            random_state=RANDOM_SEED)\n",
        "\n",
        "# 2. Turn data into tensors\n",
        "X_blob = torch.from_numpy(X_blob).type(torch.float)\n",
        "y_blob = torch.from_numpy(y_blob).type(torch.LongTensor)\n",
        "\n",
        "# 3. Split into train and test\n",
        "X_blob_train, X_blob_test, y_blob_train, y_blob_test = train_test_split(X_blob,\n",
        "                                                                        y_blob,\n",
        "                                                                        test_size=0.2, \n",
        "                                                                        random_state=RANDOM_SEED)\n",
        "\n",
        "# 4. Plot data (visualize, visualize, visualize)\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.scatter(X_blob[:, 0], X_blob[:, 1], c=y_blob, cmap=plt.cm.RdYlBu);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "G3rlb9omR7dx",
        "outputId": "3e9cafc7-3f17-4c32-89f9-2779a6f98da0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.2 Building a multi-class classification model in PyTorch"
      ],
      "metadata": {
        "id": "cxa4vNR0R7ax"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create device agnostic code\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Is3TPvOIR7SX",
        "outputId": "84cbb9cc-24fc-4907-e03d-4e70ba2aff26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a multi-class classification model\n",
        "class BlobModel(nn.Module):\n",
        "  def __init__(self, input_features, output_features, hidden_units=8):\n",
        "    \"\"\"Initializes multi-class classification model.\n",
        "    \n",
        "    Args:\n",
        "      input_features (int): Number of input features to the model\n",
        "      output_features (int): Number of outputs features (number of output classes)\n",
        "      hidden_units (int): Number of hidden units between layers, default 8\n",
        "\n",
        "    Returns:\n",
        "\n",
        "    Example:\n",
        "    \"\"\"\n",
        "    super().__init__()\n",
        "    self.linear_layer_stack = nn.Sequential(\n",
        "        nn.Linear(in_features=input_features, out_features=hidden_units),\n",
        "        # nn.ReLU(),\n",
        "        nn.Linear(in_features=hidden_units, out_features=hidden_units),\n",
        "        # nn.ReLU(),\n",
        "        nn.Linear(in_features=hidden_units, out_features=output_features)\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.linear_layer_stack(x)\n",
        "\n",
        "# Create an instance of BlobModel and send it to the target device\n",
        "model_4 = BlobModel(input_features=2,\n",
        "                    output_features=4,\n",
        "                    hidden_units=8).to(device)\n",
        "\n",
        "model_4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KYMrjuCdHHb",
        "outputId": "ab48ffe1-de0d-4f83-c9fa-3a80314950df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BlobModel(\n",
              "  (linear_layer_stack): Sequential(\n",
              "    (0): Linear(in_features=2, out_features=8, bias=True)\n",
              "    (1): Linear(in_features=8, out_features=8, bias=True)\n",
              "    (2): Linear(in_features=8, out_features=4, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_blob_train.shape, y_blob_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFjKF1qHdHBv",
        "outputId": "349d9f53-75ae-47d6-9be9-42b28ca88681"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([800, 2]), tensor([1, 0, 2, 2, 0], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.unique(y_blob_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cnvNS0YdGNV",
        "outputId": "a9b97a2a-f4d6-456c-d89c-f893d0107e27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0, 1, 2, 3], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.3 Create a loss function and an optimizer for a multi-class classification model"
      ],
      "metadata": {
        "id": "0zNAjKSaelX4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a loss function for multi-class classification - loss function measures how wrong our model's predictions are\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "# Create an optimizer for multi-class classification - optimizer updates our model parameters to try and reduce the loss\n",
        "optimizer = torch.optim.SGD(params=model_4.parameters(), \n",
        "                            lr=0.1) # learning rate is a hyperparameter you can change"
      ],
      "metadata": {
        "id": "npZosTbqf8jI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.4 Getting prediction probabilities for a multi-class PyTorch model\n",
        "\n",
        "In order to evaluate and train and test our model, we need to convert our model's outputs (logtis) to predicition probabilities and then to prediction labels.\n",
        "\n",
        "Logits (raw output of the model) -> Pred probs (use `torch.softmax`) -> Pred labels (take the argmax of the prediction probabilities)"
      ],
      "metadata": {
        "id": "hc8BN4MghEpG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's get some raw outputs of our model (logits)\n",
        "model_4.eval()\n",
        "with torch.inference_mode():\n",
        "  y_logits = model_4(X_blob_test.to(device))\n",
        "\n",
        "y_logits[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3c3XxhShMnO",
        "outputId": "321fe81b-d498-4cb3-f5a0-a678cd0d60af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-1.2549, -0.8112, -1.4795, -0.5696],\n",
              "        [ 1.7168, -1.2270,  1.7367,  2.1010],\n",
              "        [ 2.2400,  0.7714,  2.6020,  1.0107],\n",
              "        [-0.7993, -0.3723, -0.9138, -0.5388],\n",
              "        [-0.4332, -1.6117, -0.6891,  0.6852],\n",
              "        [ 2.0878, -1.3728,  2.1248,  2.5052],\n",
              "        [ 1.8310,  0.8851,  2.1674,  0.6006],\n",
              "        [ 0.1412, -1.4742, -0.0360,  1.0373],\n",
              "        [ 2.9426,  0.7047,  3.3670,  1.6184],\n",
              "        [-0.0645, -1.5006, -0.2666,  0.8940]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_blob_test[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WKQwb_4aiIxn",
        "outputId": "57801c4f-52cf-4359-9d33-7dfa0ca7d734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 3, 2, 1, 0, 3, 2, 0, 2, 0], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert our model's logit outputs to prediction probabilities\n",
        "y_pred_probs = torch.softmax(y_logits, dim=1)\n",
        "print(y_logits[:5])\n",
        "print(y_pred_probs[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBPtpiGjhyEP",
        "outputId": "f1f581ec-61f0-44e5-fe06-29186e49a150"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-1.2549, -0.8112, -1.4795, -0.5696],\n",
            "        [ 1.7168, -1.2270,  1.7367,  2.1010],\n",
            "        [ 2.2400,  0.7714,  2.6020,  1.0107],\n",
            "        [-0.7993, -0.3723, -0.9138, -0.5388],\n",
            "        [-0.4332, -1.6117, -0.6891,  0.6852]], device='cuda:0')\n",
            "tensor([[0.1872, 0.2918, 0.1495, 0.3715],\n",
            "        [0.2824, 0.0149, 0.2881, 0.4147],\n",
            "        [0.3380, 0.0778, 0.4854, 0.0989],\n",
            "        [0.2118, 0.3246, 0.1889, 0.2748],\n",
            "        [0.1945, 0.0598, 0.1506, 0.5951]], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert our model's prediction probabilities to prediction labels\n",
        "y_preds = torch.argmax(y_pred_probs, dim=1)\n",
        "y_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkH214Emh_Im",
        "outputId": "817d61b8-079a-4327-d1cf-abe4131614fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([3, 3, 2, 1, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2,\n",
              "        2, 2, 3, 3, 3, 3, 3, 1, 1, 2, 1, 2, 1, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3,\n",
              "        3, 3, 1, 3, 3, 1, 3, 2, 3, 1, 3, 2, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3,\n",
              "        3, 3, 2, 3, 3, 3, 3, 1, 3, 2, 3, 2, 3, 3, 2, 3, 3, 2, 3, 3, 1, 3, 3, 3,\n",
              "        1, 3, 3, 2, 3, 3, 3, 3, 2, 3, 1, 3, 3, 2, 1, 1, 3, 2, 2, 3, 3, 3, 1, 2,\n",
              "        2, 3, 3, 1, 2, 3, 3, 3, 2, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 1, 1, 3, 2, 2,\n",
              "        2, 2, 3, 3, 3, 2, 2, 1, 3, 2, 3, 3, 3, 3, 2, 3, 2, 3, 3, 2, 3, 3, 2, 3,\n",
              "        2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 3, 1, 3, 2, 2, 3, 2, 2, 3, 3, 2, 2, 3, 3,\n",
              "        1, 3, 2, 3, 3, 1, 2, 3], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_blob_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mzRST_n8i8yV",
        "outputId": "51b41786-ad3c-45ee-b700-25995885bd0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 3, 2, 1, 0, 3, 2, 0, 2, 0, 0, 1, 0, 0, 0, 3, 3, 2, 3, 3, 3, 0, 1, 2,\n",
              "        2, 2, 3, 0, 1, 0, 3, 1, 1, 3, 1, 2, 1, 3, 0, 2, 0, 3, 3, 2, 0, 3, 1, 1,\n",
              "        0, 3, 1, 0, 1, 1, 3, 2, 1, 1, 3, 2, 2, 0, 3, 2, 2, 0, 0, 3, 3, 0, 0, 3,\n",
              "        3, 3, 2, 3, 3, 3, 3, 1, 0, 2, 3, 2, 3, 3, 2, 3, 3, 2, 3, 3, 1, 3, 3, 3,\n",
              "        1, 0, 3, 2, 0, 0, 3, 0, 2, 3, 1, 0, 3, 2, 1, 1, 0, 2, 2, 3, 0, 0, 1, 2,\n",
              "        2, 3, 0, 1, 2, 0, 0, 0, 2, 3, 1, 2, 3, 2, 0, 3, 0, 0, 1, 1, 1, 0, 2, 2,\n",
              "        2, 2, 0, 3, 3, 2, 2, 1, 3, 2, 0, 0, 3, 3, 2, 1, 2, 0, 3, 2, 0, 3, 2, 0,\n",
              "        2, 2, 2, 0, 3, 1, 1, 1, 1, 1, 3, 1, 0, 2, 2, 1, 2, 2, 0, 1, 2, 2, 0, 0,\n",
              "        1, 3, 2, 0, 3, 1, 2, 1], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.5 Creating a training loop and testing loop for a multi-class PyTorch model"
      ],
      "metadata": {
        "id": "vNUDES0mi8v-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_blob_train.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfFzD8T0m49e",
        "outputId": "c66fb98a-d515-44dd-da02-40e60fc0ef5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.int64"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the multi-class model to the data\n",
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)\n",
        "\n",
        "# Set number of epochs \n",
        "epochs = 100\n",
        "\n",
        "# Put data to the target device\n",
        "X_blob_train, y_blob_train = X_blob_train.to(device), y_blob_train.to(device)\n",
        "X_blob_test, y_blob_test = X_blob_test.to(device), y_blob_test.to(device)\n",
        "\n",
        "# Loop through data\n",
        "for epoch in range(epochs):\n",
        "  ### Training \n",
        "  model_4.train()\n",
        "\n",
        "  y_logits = model_4(X_blob_train)\n",
        "  y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1)\n",
        "\n",
        "  loss = loss_fn(y_logits, y_blob_train)\n",
        "  acc = accuracy_fn(y_true=y_blob_train,\n",
        "                    y_pred=y_pred)\n",
        "  \n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "\n",
        "  ### Testing\n",
        "  model_4.eval()\n",
        "  with torch.inference_mode():\n",
        "    test_logits = model_4(X_blob_test)\n",
        "    test_preds = torch.softmax(test_logits, dim=1).argmax(dim=1)\n",
        "    \n",
        "    test_loss = loss_fn(test_logits, y_blob_test)\n",
        "    test_acc = accuracy_fn(y_true=y_blob_test,\n",
        "                           y_pred=test_preds)\n",
        "    \n",
        "  # Print out what's happenin'\n",
        "  if epoch % 10 == 0:\n",
        "    print(f\"Epoch: {epoch} | Loss: {loss:.4f}, Acc: {acc:.2f}% | Test loss: {test_loss:.4f}, Test acc: {test_acc:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CzQG_INbi8tG",
        "outputId": "7734d84e-e834-46c1-c8b8-0c2d576ef7e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Loss: 1.0432, Acc: 65.50% | Test loss: 0.5786, Test acc: 95.50%\n",
            "Epoch: 10 | Loss: 0.1440, Acc: 99.12% | Test loss: 0.1304, Test acc: 99.00%\n",
            "Epoch: 20 | Loss: 0.0806, Acc: 99.12% | Test loss: 0.0722, Test acc: 99.50%\n",
            "Epoch: 30 | Loss: 0.0592, Acc: 99.12% | Test loss: 0.0513, Test acc: 99.50%\n",
            "Epoch: 40 | Loss: 0.0489, Acc: 99.00% | Test loss: 0.0410, Test acc: 99.50%\n",
            "Epoch: 50 | Loss: 0.0429, Acc: 99.00% | Test loss: 0.0349, Test acc: 99.50%\n",
            "Epoch: 60 | Loss: 0.0391, Acc: 99.00% | Test loss: 0.0308, Test acc: 99.50%\n",
            "Epoch: 70 | Loss: 0.0364, Acc: 99.00% | Test loss: 0.0280, Test acc: 99.50%\n",
            "Epoch: 80 | Loss: 0.0345, Acc: 99.00% | Test loss: 0.0259, Test acc: 99.50%\n",
            "Epoch: 90 | Loss: 0.0330, Acc: 99.12% | Test loss: 0.0242, Test acc: 99.50%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.6 Making and evaluating predictions with a PyTorch multi-class model"
      ],
      "metadata": {
        "id": "7GsIPbD3l81j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "model_4.eval()\n",
        "with torch.inference_mode():\n",
        "  y_logits = model_4(X_blob_test)\n",
        "\n",
        "# View the first 10 predictions\n",
        "y_logits[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcLD0Iqal84o",
        "outputId": "7ef95756-377f-4bf7-9fee-0580e029390f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[  4.3377,  10.3539, -14.8948,  -9.7642],\n",
              "        [  5.0142, -12.0371,   3.3860,  10.6699],\n",
              "        [ -5.5885, -13.3448,  20.9894,  12.7711],\n",
              "        [  1.8400,   7.5599,  -8.6016,  -6.9942],\n",
              "        [  8.0726,   3.2906, -14.5998,  -3.6186],\n",
              "        [  5.5844, -14.9521,   5.0168,  13.2890],\n",
              "        [ -5.9739, -10.1913,  18.8655,   9.9179],\n",
              "        [  7.0755,  -0.7601,  -9.5531,   0.1736],\n",
              "        [ -5.5918, -18.5990,  25.5309,  17.5799],\n",
              "        [  7.3142,   0.7197, -11.2017,  -1.2011]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Go from logits -> Prediction probabilities\n",
        "y_pred_probs = torch.softmax(y_logits, dim=1)\n",
        "y_pred_probs[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nsn_2gE5l9SG",
        "outputId": "b1aa2905-f5f3-41f9-acc5-c9375812abf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2.4332e-03, 9.9757e-01, 1.0804e-11, 1.8271e-09],\n",
              "        [3.4828e-03, 1.3698e-10, 6.8363e-04, 9.9583e-01],\n",
              "        [2.8657e-12, 1.2267e-15, 9.9973e-01, 2.6959e-04],\n",
              "        [3.2692e-03, 9.9673e-01, 9.5436e-08, 4.7620e-07],\n",
              "        [9.9168e-01, 8.3089e-03, 1.4120e-10, 8.2969e-06],\n",
              "        [4.5039e-04, 5.4288e-13, 2.5532e-04, 9.9929e-01],\n",
              "        [1.6306e-11, 2.4030e-13, 9.9987e-01, 1.3003e-04],\n",
              "        [9.9860e-01, 3.9485e-04, 5.9938e-08, 1.0045e-03],\n",
              "        [3.0436e-14, 6.8305e-20, 9.9965e-01, 3.5218e-04],\n",
              "        [9.9843e-01, 1.3657e-03, 9.0768e-09, 2.0006e-04]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Go from pred probs to pred labels\n",
        "y_preds = torch.argmax(y_pred_probs, dim=1)\n",
        "y_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7fXmpC-l9PI",
        "outputId": "fcc2ef7b-d985-4c21-a265-b290546a439d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 3, 2, 1, 0, 3, 2, 0, 2, 0], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_4, X_blob_train, y_blob_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_4, X_blob_test, y_blob_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "cvciOiS6q0Jo",
        "outputId": "4ab64509-f5b0-4034-936a-e240d311c4a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9. A few more classification metrics... (to evaluate our classification model)\n",
        "\n",
        "* Accuracy - out of 100 samples, how many does our model get right?\n",
        "* Precision\n",
        "* Recall \n",
        "* F1-score\n",
        "* Confusion matrix\n",
        "* Classification report\n",
        "\n",
        "See this article for when to use precision/recall - https://towardsdatascience.com/beyond-accuracy-precision-and-recall-3da06bea9f6c\n",
        "\n",
        "If you want access to a lot of PyTorch metrics, see TorchMetrics - https://torchmetrics.readthedocs.io/en/latest/ "
      ],
      "metadata": {
        "id": "YyD62KSPq0HD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torchmetrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUPfxaG7l869",
        "outputId": "7ce5069c-ef1f-4bda-c3c5-f76067d390e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torchmetrics\n",
            "  Downloading torchmetrics-0.7.2-py3-none-any.whl (397 kB)\n",
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            "\u001b[?25hRequirement already satisfied: torch>=1.3.1 in /usr/local/lib/python3.7/dist-packages (from torchmetrics) (1.10.0+cu111)\n",
            "Collecting pyDeprecate==0.3.*\n",
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            "Installing collected packages: pyDeprecate, torchmetrics\n",
            "Successfully installed pyDeprecate-0.3.2 torchmetrics-0.7.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchmetrics import Accuracy\n",
        "\n",
        "# Setup metric\n",
        "torchmetric_accuracy = Accuracy().to(device)\n",
        "\n",
        "# Calculuate accuracy\n",
        "torchmetric_accuracy(y_preds, y_blob_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsKTgOmhlqyJ",
        "outputId": "9e4c71ee-d08b-4769-b2df-c358c0fbc316"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.9950, device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torchmetric_accuracy.device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cST55cwuU4V",
        "outputId": "9e553f99-e367-48f4-9e8b-e11fda8f060a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercises & Extra-curriculum \n",
        "\n",
        "See exercises and extra-curriculum here: https://www.learnpytorch.io/02_pytorch_classification/#exercises"
      ],
      "metadata": {
        "id": "vf_cd5t5uZQh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ycgmZrSGvf_l"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}